Ultrafast Multivalley Optical Switching in Germanium for High-Speed Computing and Communications

Researchers demonstrate ultrafast transparency switching across multiple wavelengths using single laser excitation in germanium

Multicolored optical switching is essential for potential advancements in telecommunication and optical computing. However, most materials typically exhibit only single-colored optical nonlinearity under intense laser illumination. To address this, researchers have demonstrated that exciting the multivalley semiconductor germanium with a single-color pulse laser enables ultrafast transparency switching across multiple wavelengths. This breakthrough could drive the development of ultrafast optical switches for future multiband communication and optical computing.

Opaque materials can transmit light when excited by a high-intensity laser beam. This process, known as optical bleaching, induces a nonlinear effect that temporarily alters the properties of a material. Remarkably, when the laser is switched on and off at ultrahigh speeds, the effect can be dynamically controlled, opening new possibilities for advanced optical technologies.

Multicolored optical switching is an important phenomenon with potential applications in fields such as telecommunications and optical computing. However, most materials typically exhibit single-color optical nonlinearity under intense laser illumination, limiting their use in systems requiring multicolor or multiband switching capabilities. Currently, most optical switches are based on microelectromechanical systems, which require an electric voltage or current to operate, resulting in slow response times.

To address this gap, a group of researchers, led by Professor Junjun Jia from the Faculty of Science and Engineering at Waseda University, Japan, in collaboration with Professor Hui Ye and Dr. Hossam A. Almossalami from the College of Optical Science and Engineering at Zhejiang University, China, Professor Naoomi Yamada from the Department of Applied Chemistry at Chubu University, Japan, and Dr. Takashi Yagi from the National Institute of Advanced Industrial Science and Technology, Japan, investigated the multivalley optical switching phenomenon in germanium (Ge) films. They focused on how intense laser irradiation induces ultrafast optical switching across multiple wavelengths in Ge, a multivalley semiconductor. Their study demonstrated efficient multicolored optical switching using a single-color pulse laser, potentially overcoming the limitations of traditional single-color optical nonlinearities. Their research was published in Physical Review Applied on February 24, 2025.

By irradiating Ge with an intense pulse laser, the team achieved ultrafast switching between transparency and opacity across a wide wavelength range. Femtosecond time-resolved transient transmission measurements revealed ultrafast optical switching in both the Γ and L valleys, due to the existence of intravalley and intervalley scattering. “Our results confirm that intense laser irradiation in Ge films allows for ultrafast optical switching across multiple wavelengths, offering the possibility of controlling a material’s transparency and opening new doors for possible applications in optical communications, optical computing, and beyond,” explains Prof. Jia.

Such multivalley optical switching is found to strongly depend on the band structure of Ge. Experimental measurements suggest that the transient signal is highly dependent on the specific region of the band structure involved. For example, the transient transmission spectra reveal a split-off energy of 240 meV at the L high symmetric point. “Careful selection of probing energies, based on the band dispersion calculated with the HSE06 functional and spin-orbit coupling effects, allowed us to accurately capture the transient electronic occupation in both the Γ and L valleys,” says Prof. Jia. This allows the extraction of intervalley and intravalley scattering times in multivalley materials from transient measurements.

Overall, this study highlights the significant potential of Ge as a key material for advanced optical switching, with promising applications in high-speed data transmission and computing. By enabling control over transparency at multiple wavelengths using a single-color pulse laser, exciting possibilities open up for the development of ultrafast optical switches. “This finding is expected to address the growing demand for higher data rates and security in the face of increasing internet traffic, marking a key step forward in the advancement of ultrafast optical switching devices,” concludes Prof. Jia.

Reference

Authors: Junjun Jia¹, Hossam A. Almossalami², Hui Ye², Naoomi Yamada³, Takashi Yagi⁴
Title of original paper: Multivalley optical switching in germanium
Journal: Physical Review Applied
DOI: 10.1103/PhysRevApplied.23.024060
Article Publication Date:24 February 2025
Affiliations:
¹Global Center for Science and Engineering (GCSE), Faculty of Science and Engineering, Waseda University, Japan
²College of Optical Science and Engineering, Zhejiang University, China
³Department of Applied Chemistry, Chubu University, Japan
⁴National Metrology Institute of Japan (NMIJ), National Institute of Advanced Industrial Science and Technology (AIST), Japan

About Professor Junjun Jia

Junjun Jia is a Professor at the Faculty of Science and Engineering, Waseda University, Japan. He earned his Ph.D. from the University of Tokyo in 2011. His research focuses on the design and fabrication of functional solid-state materials, as well as the development of solid-state devices, including solid-state thermal circuital elements, acoustic wave-based devices, and nonequilibrium electronic devices. His interests include nonlinear optics, non-equilibrium physics, and excited electronic/phonon structure in solids materials. Dr. Jia has published extensively in peer-reviewed journals such as Advanced Functional Materials, Physical Review B, Physical Review Applied. He has received several awards, including the Waseda e-Teaching Award in 2022. He is a member of various committees, including the Materials Research Society of Japan.

An Efficient Self-Assembly Process for Advanced Self-Healing Materials

The novel method produces multilayered self-healing films with enhanced durability compared to conventional materials for coatings and flexible electronics

Self-healing coatings are advanced materials that can repair damage, such as scratches and cracks on their own. Researchers from Waseda University have developed an efficient method for preparing self-healing films consisting of alternating layers of highly cross-linked organosiloxane and linear polydimethylsiloxane (PDMS). Their film is more durable than conventional self-healing PDMS materials, offering superior hardness and greater thermal stability while self-healing at mild temperatures, paving the way for stronger, more reliable, and easier-maintained self-healing materials.

Polysiloxane materials, such as polydimethylsiloxane (PDMS)-based elastomers, exhibit a self-healing capability by the introduction of silanolate (Si–O^–) groups. This ability stems from their dynamic siloxane (Si–O–Si) bonds, which can break and reform to repair damage. Their self-healing properties could make them valuable in applications like protective coatings for use in various fields, such as optics, electronics, and aerospace.

To improve the properties of PDMS-based materials, they have been combined with inorganic fillers such as nanoparticles or nanosheets. Generally, the introduction of nanosheets into polymers leads to the formation of a layered structure that exhibits superior thermal, mechanical, and gas barrier properties. Furthermore, improved crack healing ability of oriented films was reported. This improvement is attributed to polymer diffusion concentrated in the in-plane direction.

Researchers at Waseda University, Japan, have made significant progress in enhancing self-healing siloxane materials by developing a more efficient method for fabricating multilayered films. In a study published on January 6, 2025, in Volume 61, Issue 16, of the journal Chemical Communications, a team led by Professor Atsushi Shimojima, with Research Associate Yoshiaki Miyamoto and Assistant Professor Takamichi Matsuno, fabricated a composite film composed of highly cross-linked organosiloxane (silsesquioxane) and grafted PDMS layers using a self-assembly process.

“Replacing traditional materials with our self-healing material, which is less susceptible to deterioration and has high hardness, would be in high demand for maintenance-free and durable applications,” says Miyamoto, the lead author of the study.

The researchers began by depositing a solution containing 1,2-bis(triethoxysilyl)ethane, Pluronic P123 (a PEO–PPO–PEO triblock copolymer, where PEO stands for poly(ethylene oxide) and PPO stands for poly(propylene oxide)), and a PEO–PDMS–PEO block copolymer onto a silicon or glass substrate using spin-coating or drop-casting techniques. This process formed a thin film with a lamellar (layered) structure.

The film was then calcinated in air at 170 °C for 4 hours, resulting in the removal of the PEO and PPO blocks. This process left behind a multilayered structure composed of silsesquioxane and PDMS layers.

To impart self-healing properties to the film, Si–O^– groups were introduced. These groups promote rearrangement and reconnection of the siloxane (Si–O–Si) networks. To achieve this, the film was immersed in a solution of tetrahydrofuran, water, and potassium hydroxide (KOH). In this process, hydroxide ions (OH^–) from KOH removed protons (H⁺) from silanol (Si–OH) groups, converting them into Si–O^– ions. The final film could repair micrometer-scale cracks when heated to 80 °C at 40% relative humidity for 24 hours.

The film showed superior properties compared to conventional PDMS-based materials. The cross-linked organosiloxane layers provided greater rigidity and served as a barrier against the volatilization of cyclic siloxanes, addressing the limitations of traditional PDMS materials. While conventional self-healing PDMS elastomers have a hardness of 49 MPa, the final self-healing film exhibited a hardness of 1.50 GPa.

“This innovative multilayered design allows our material to be both harder and more heat-resistant than existing self-healing siloxane-based materials, paving the way for more durable and reliable applications,” says Miyamoto.

With its high hardness and self-healing properties, this material is well-suited for protective coatings, flexible electronics, and other applications that require long-lasting performance.

Reference

Title of original paper:Multilayered organosiloxane films with self-healing ability converted from block copolymer nanocomposites
DOI:10.1039/D4CC05804F
Journal:Chemical Communications
Article Publication Date:06 January 2025
Authors:Yoshiaki Miyamoto¹, Takamichi Matsuno^1,2,3 and Atsushi Shimojima^1,2,3
Affiliations:
¹Department of Applied Chemistry, Faculty of Science and Engineering, Waseda University, Japan
²Waseda Research Institute for Science and Engineering, Waseda University, Japan
³Kagami Memorial Research Institute for Materials Science and Technology, Waseda University, Japan

About Dr. Yoshiaki Miyamoto from Waseda University

Dr. Yoshiaki Miyamoto is a Research Associate at the School of Advanced Science and Engineering, working under the supervision of Professor Atsushi Shimojima. His research focuses on the relationship between material structure, composition, and self-healing properties, particularly within the realm of siloxane-based materials. He explores factors such as network flexibility, swelling behavior, and dynamic bond rearrangement. His research has produced publications demonstrating expertise in designing self-healing materials with improved mechanical properties and thermal and chemical stability. Dr. Miyamoto’s research contributes to the advancement of functional materials with potential applications in coatings, adhesives, and other areas where self-healing capabilities are crucial.

Machine Learning Unlocks Superior Performance in Light-Driven Organic Crystals

LASSO regression and Bayesian optimization enhance crystal force output, advancing next-generation light-responsive actuator materials

Researchers have developed a machine learning workflow to optimize the output force of photo-actuated organic crystals. Using LASSO regression to identify key molecular substructures and Bayesian optimization for efficient sampling, they achieved a maximum blocking force of 37.0 mN—73 times more efficient than conventional methods. These findings could help develop remote-controlled actuators for medical devices and robotics, supporting applications such as minimally invasive surgery and precision drug delivery.

Materials that convert external stimuli into mechanical motion, known as actuators, play a crucial role in robotics, medical devices, and other advanced applications. Among them, photomechanical crystals deform in response to light, making them promising for lightweight and remotely controllable actuation. Their performance depends on factors such as molecular structures, crystal properties, and experimental conditions.

A key performance indicator of these materials is the blocking force—the maximum force exerted when deformation is completely restricted. However, achieving high blocking forces remains challenging due to the complex interplay of crystal characteristics and testing conditions. Understanding and optimizing these factors is essential for expanding the potential applications of photomechanical crystals.

In a step toward optimizing the output force of photo-actuated organic crystals, researchers from Waseda University have leveraged machine learning techniques to enhance their performance. The study was led by Associate Professor Takuya Taniguchi from the Center for Data Science, along with Mr. Kazuki Ishizaki and Professor Toru Asahi, both from the Department of Advanced Science and Engineering, Graduate School of Advanced Science and Engineering at Waseda University. Their findings were published online in Digital Discovery on 20 March 2025.

“We noticed that machine learning simplifies the search for optimal molecules and experimental parameters,” says Dr. Taniguchi. “This inspired us to integrate data science techniques with synthetic chemistry, enabling us to rapidly identify new molecular designs and experimental approaches for achieving high-performance results.”

In this study, the team utilized two machine learning techniques: LASSO (least absolute shrinkage and selection operator) regression for molecular design and Bayesian optimization for selecting experimental conditions. The first step led to a material pool of salicylideneamine derivatives, while the second enabled efficient sampling from this pool for real-world force measurements. As a result, the team successfully maximized the blocking force, achieving up to 3.7 times greater force output compared to previously reported values and accomplishing this at least 73 times more efficiently than conventional trial-and-error method.

“Our research marks a significant breakthrough in photo-actuated organic crystals by systematically applying machine learning,” says Dr. Taniguchi. “By optimizing both molecular structures and experimental conditions, we have demonstrated the potential to dramatically enhance the performance of light-responsive materials.”

The proposed technology has broad implications for remote-controlled actuators, small-scale robotics, medical devices, and energy-efficient systems. Because photo-actuated crystals respond to light, they enable contactless and remote operation, making them ideal robotic components working in confined or sensitive environments. Their ability to generate force noninvasively with focused light could also be valuable for microsurgical tools and drug delivery mechanisms that require precise, remote actuation.

By leveraging a cleaner energy input—light irradiation—while maximizing mechanical output, these materials hold promise for eco-friendly manufacturing processes and devices aimed at reducing overall energy consumption. “Beyond improving force output, our approach paves the way for more sophisticated, miniaturized devices, from wearable technology to aerospace engineering and remote environmental monitoring,” Dr. Taniguchi adds.

In conclusion, this study highlights the power of a machine learning–driven strategy in accelerating the development of high-performance photo-actuated materials, bringing them one step closer to real-world applications and commercial viability.

Reference

Authors: Kazuki Ishizaki¹, Toru Asahi¹, and Takuya Taniguchi²
Title of original paper: Machine Learning-Driven Optimization of Output Force in Photo-Actuated Organic Crystals
Journal: Digital Discovery
DOI:10.1039/D4DD00380B
Article Publication Date:20 March 2025
Affiliations:
¹Department of Advanced Science and Engineering, Graduate School of Advanced Science and Engineering, Waseda University
²Center for Data Science, Waseda University

About Associate Professor Takuya Taniguchi from Waseda University

Takuya Taniguchi is an Associate Professor at the Center for Data Science at Waseda University, Japan. He received a Doctor of Engineering degree from the Department of Advanced Science and Engineering, Graduate School of Advanced Science and Engineering, Waseda University, in 2019. His research areas of interest include structural organic chemistry, physical organic chemistry, organic functional materials, materials informatics, and materials science. His publications have received over 500 citations.